Cell competition is defined as the context-dependent elimination of cells that is mediated by intercellular communication, such as paracrine or contact-dependent cell signaling, and/or mechanical stresses. It is considered to be a quality control mechanism that facilitates the removal of suboptimal cells from both adult and embryonic tissues. Cell competition, however, can also be hijacked by transformed cells to acquire a ‘super-competitor’ status and outcompete the normal epithelium to establish a precancerous field. To date, many genetic drivers of cell competition have been identified predominately through studies in Drosophila. Especially during the last couple of years, ethylmethanesulfonate-based genetic screens have been instrumental to our understanding of the molecular regulators behind some of the most common competition mechanisms in Drosophila, namely competition due to impaired ribosomal function (or anabolism) and mechanical sensitivity. Despite recent findings in Drosophila and in mammalian models of cell competition, the drivers of mammalian cell competition remain largely elusive. Since the discovery of CRISPR/Cas9, its use in functional genomics has been indispensable to uncover novel cancer vulnerabilities. We envision that CRISPR/Cas9 screens will enable systematic, genome-scale probing of mammalian cell competition to discover novel mutations that not only trigger cell competition but also identify novel molecular components that are essential for the recognition and elimination of less fit cells. In this review, we summarize recent contributions that further our understanding of the molecular mechanisms of cell competition by genetic screening in Drosophila, and provide our perspective on how similar and novel screening strategies made possible by whole-genome CRISPR/Cas9 screening can advance our understanding of mammalian cell competition in the future.
In this paper Gradient Flow methods are used to solve systems of Differential-Algebraic Equations via a novel reformulation strategy, focusing on the solution of index-1 Differential-Algebraic Equation systems. A reformulation is first effected on semi-explicit index-1 Differential-Algebraic Equation systems, which casts them as pure Ordinary Differential Equation systems subject to an embedded pointwise least-squares problem. This is then formulated as a Gradient Flow optimization problem. Rigorous proofs for this novel scheme are provided for asymptotic and epsilon convergence. The computational results validate the predictions of the effectiveness of the proposed approach, with efficient and accurate solutions obtained for the case studies considered. Beyond the theoretical and practical value for the solution of DAE systems as pure ODE ones, the methodology is expected to have an impact in similar cases where an ODE system is subjected to algebraic constraints, such as the Hamiltonian necessary conditions of optimality in Optimal Control problems.
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